Outline. Introduction to QualySense QSorter Technology Evaluating quality in Rice Our technology on Rice Contact details

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2 Outline Introduction to QualySense QSorter Technology Evaluating quality in Rice Our technology on Rice Contact details 2

3 Our vision We commit to bringing excellence in grain, seed and bean sorting all over the world.

4 28 Quick facts about QualySense

5 Our competences Mechatronics Sensing technologies Biochemistry Big Data analysis Food Science Aerospace Engineering 5

6 QualySense Video QualySense introduction video 6

7 QSORTER TECHNOLOGY The first life science robot able to analyse and sort up to 50 kernels a second

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9 High-speed one by one analysis and sorting of grains Up to 50 kernels/second 9

10 by their physical and biochemical properties at once VARIOUS COMPOSITIONAL PROPERTIES INSECT DAMAGE BROKEN KERNELS FATTY ACIDS SUGAR 3.65 mm 6.48 mm GEOMETRY DISEASES (FUSARIUM) PROTEIN FRAGMENT ANTIOXIDANTS IDENTIFY IMPURITIES 10

11 High quality data with 3D imaging and high-resolution NIR TOP VIEW FRONT VIEW BOTTOM VIEW 3D image - wheat kernel NIR spectra for oat, wheat and barley 11

12 We acquire information for the entire batch 12

13 EVALUATING QUALITY IN RICE

14 Goals and stages We developed a rapid analysis tool to measure and sort different quality parameters in rice. Chalkiness Broken kernels Size, shape and volume Moisture, protein content Immature, damaged kernels Non-chalky rice Chalky rice 14

15 From rough to quality rice Rough rice Brown rice Milled rice High quality rice 15

16 Goals and stages Project split in three stages: Stage 1: chalkiness, broken, geometry, fissuring Stage II: moisture, protein, amylose Stage III: defects, whiteness and greenness 16

17 Length classes Long Long broken Medium Medium broken Short Short broken CONFIDENTIAL INFORMATION We reserve all rights in this document and in the information contained therein. Reproduction, use or disclosure to third parties without express authority is strictly forbidden. QualySense AG 17

18 Broken grains within different length categories Combining length and shape parameters SHORT LONG BROKEN Separation by length Separation by shape CONFIDENTIAL INFORMATION We reserve all rights in this document and in the information contained therein. Reproduction, use or disclosure to third parties without express authority is strictly forbidden. QualySense AG 18

19 Soft thresholding method for chalkiness determination Global thresholding chalky if f x t f"(x) = ( non chalky if f(x) > t Soft thresholding: Reflects gradual transition from chalky to non-chalky pixels in the thresholding region. Transfer function and weights around the chalkiness threshold Membership weights Greyscale levels Area of pixels classified as chalky vs total area determines the chalkiness of each rice kernel 19

20 Chalkiness 3D images of one grain, for all chalkiness classes. Translucent 1%-10% 10%-25% 25%-50% 50%-75% 75%-100% 20

21 Performance 125 batch samples analysed and performance compared with data from the Cervitec 120 Total chalkiness R² = CONFIDENTIAL INFORMATION We reserve all rights in this document and in the information contained therein. Reproduction, use or disclosure to third parties without express authority is strictly forbidden. QualySense AG 21

22 To be soon finalised Fissures detection in rice CONFIDENTIAL INFORMATION We reserve all rights in this document and in the information contained therein. Reproduction, use or disclosure to third parties without express authority is strictly forbidden. QualySense AG 22

23 Sorted sample according to 50% of chalkiness BIN 1 Unclassified grains BIN 2 Grains more than 50% chalky CONFIDENTIAL INFORMATION We reserve all rights in this document and in the information contained therein. Reproduction, use or disclosure to third parties without express authority is strictly forbidden. QualySense AG BIN 3 Grains less than 50% chalky 23

24 Sorted sample according to broken grains BIN 1 BIN 2 BIN 3 Unclassified grains Head grains Broken grains CONFIDENTIAL INFORMATION We reserve all rights in this document and in the information contained therein. Reproduction, use or disclosure to third parties without express authority is strictly forbidden. QualySense AG 24

25 OUR TECHNOLOGY ON RICE Using the Qsorter Explorer

26 QSE quality evaluation of rice 1. Sorting Bin 1 Damaged Discoloured Deformed Bin 2 Head grains Bin 3 Broken grains 26

27 QSE quality evaluation of rice 2. Data collected during sorting Single-kernel report - % broken, % rejects, % head, and total number of grains - length, width, breath and respective std deviation - weight prediction - chalk overall % and chalk % in five classes - % head grains that are cracked - NIR parameters such as moisture and protein. Average of batch report 27

28 QSE quality evaluation of rice 3. Rice milling quality Amount of broken kernels > HRY Amount of defected, damaged kernels > rice grading In one pass: sorting data analysis rice grading 28

29 Contact Us Unterrietstrasse 2A 8152 Glattbrugg Switzerland 29

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